Some housekeeping (again), installing necessary packages.

list.of.packages <- c("igraph", "tidygraph", "ggraph")

new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
rm(list.of.packages, new.packages)

Introduction

Networks… So what?

So, before we talk about networks, one thing upfront… why should we? I mean, they undeniably look pretty, don’t they?

Somehow, the visualization of networks fascinates the human mind (find a short TED talk on networks and how they depict our world here), and has even inspired an own art movement, networkism (see some examples here).

Yet, besides that, is there an analytical value for a data scientist to bother about networks?

Networks in R

There are a number of applications designed for network analysis and the creation of network graphs such as gephi and cytoscape. Though not specifically designed for it, R has developed into a powerful tool for network analysis.

Significant network analysis packages for R include the network, sna, and igraph package. In addition, Thomas Lin Pedersen has recently released the tidygraph package that leverage the power of igraph in a manner consistent with the tidyverse workflow. Even better, he tops it up with ggraph, a consistent ´ggplot2´-look-and-feel network visualization package.

R can also be used to make interactive network graphs with the htmlwidgets framework that translates R code to JavaScript. Cool implementations thereof are the vizNetwork and networkD3 packages.

As analytical tool, I will in this lab mostly use igraph. In terms of functions, it is pretty much equivalent to network, yet slightly more powerful, better integrated, and maintained. Since both packages have many of the same functions, better don’t load them both at once.

The Basic Structure of Networks

The Basic Jargon

First of all, what is a network? Plainly speaking, a network is a system of elements which are connected by some relationship. The vocabulary can be a bit technical and even inconsistent between different disciplines, packages, and software. The whole system is (surprise, surprise) usually called a network or graph. The elements are commonly referred to as nodes (system theory jargon) or vertices (graph theory jargon) of a graph, while the connections are edges or links. I will mostly refer to the elements as nodes, and their connections as edges.

Generally, networks are a form of representing relational data. This is a very general tool that can be applied to many different types of relationships between all kind of elements. The content, meaning, and interpretation for sure depends on what elements we display, and which types of relationships. For example:

  • In Social Network Analysis:
    • Nodes represent actors (which can be persons, firms and other socially constructed entities)
    • Edges represent relationships between this actors (friendship, interaction, co-affiliation, similarity ect.)
  • Other types of network
    • Chemistry: Interaction between molecules
    • Computer Science: The wirld-wide-web, inter- and intranet topologies
    • Biology: Food-web, ant-hives

The possibilities to depict relational data are manifold. For example:

  • Relations among persons
    • Kinship: mother of, wife of…
    • Other role based: boss of, supervisor of…
    • Cognitive/perceptual: knows, aware of what they know…
    • Affective: likes, trusts…
    • Interaction: give advice, talks to…
    • Affiliation: belong to same clubs, shares same interests…
  • Relations among organizations
    • As corporate entities
    • Buy from / sell to, leases to, outsources to
    • Owns shares of, subsidiary of
    • Joint ventures, strategic alliances
    • Via their members
      • Personnel flows
      • Interlocking directorates
      • Personal friendships
      • Co-memberships
  • Relations other (non-social) entities
    • Patents
      • Patents citing other patents
      • Co-occurrence of technological classes *Research fields
      • through citations
      • through people co-affiliated with fields *Sectors
      • input-output relations
      • Labor mobility *Technologies
      • Patent IPC classes
      • Semantic co-occurrence

Note: Content matters! Each relation yields a different structure & has different effects. Theories might make sense on inter-personal, but not inter-organizational or non-social context.

The Data-Structure of Relational Data

Edgelist

MOst real world relational data is to be found in what we call an edge list, a dataframe that contains a minimum of two columns, one column of nodes that are the source of a connection and another column of nodes that are the target of the connection. The nodes in the data are identified by unique IDs. If the distinction between source and target is meaningful, the network is directed. If the distinction is not meaningful, the network is undirected (more on that later). So, every row that contains the ID of one element in column 1, and the ID of another element in column 2 indicates that a connection between them exists. An edge list can also contain additional columns that describe attributes of the edges such as a magnitude aspect for an edge. If the edges have a magnitude attribute the graph is considered weighted (more on that later). Below an example ofa minimal edge list created with the tibble() function.

edge_list <- tibble(from = c(1, 2, 2, 3, 4), to = c(2, 3, 4, 2, 1))
edge_list

Sometimes it is preferable to also create a separate node list. At its simplest, a node list is a data frame with a single column - which I will label as “id” - that lists the node IDs found in the edge list. The advantage of creating a separate node list is the ability to add attribute columns to the data frame such as the names of the nodes or any kind of groupings.

library(tidyverse)
node_list <- tibble(id = 1:4, group = sample(letters[1:2], 4, replace = TRUE))
node_list

Adjacency Matrix

A second popular form of network representation is the adjacency-matrix (also called socio-matrix). It is represented as a \(n*n\) matrix, where \(n\) stands for the number of elements of which their relationships should be represented. The value in the cell that intercepts row \(n\) and column \(m\) indicates if an edge is present (=1) or absent (=0).

Tip: Given an edgelist, an adjacency matrix can easily be produced by crosstabulating:

adj_matrix <- table(edge_list) %>% as.matrix()
adj_matrix
##     to
## from 1 2 3 4
##    1 0 1 0 0
##    2 0 0 1 1
##    3 0 1 0 0
##    4 1 0 0 0

Generating a Graph Object in igraph

To create an igraph object from an edge-list data frame we can use the graph_from_data_frame() function, which is a bit more straight forward than network(). There are three arguments in the graph_from_data_frame() function: d, vertices, and directed. Here, d refers to the edge list, vertices to the node list, and directed can be either TRUE or FALSE depending on whether the data is directed or undirected. By default, graph.data.frame() treats the first two columns of the edge list and any remaining columns as edge attributes.

library(igraph)
g <- graph_from_data_frame(d = edge_list, vertices = node_list, directed = FALSE)
g
## IGRAPH c2a0ee9 UN-- 4 5 -- 
## + attr: name (v/c), group (v/c)
## + edges from c2a0ee9 (vertex names):
## [1] 1--2 2--3 2--4 2--3 1--4

Lets inspect the resulting object. An igraph graph object summary reveals some interesting informations.

  • First, it tells us the graph-type: undirected UN, or directed DN
  • Afterwards, the number of nodes (4), and edges (6)
  • Followed by the node attributes (node level variables), which in this case are only their name (attr: name (v/c))
  • Lastly, a list of all existing edges. Note: n--m indicates an undirected, n->m an directed edge.

Lets take a look at the structure of the object:

glimpse(g[[1]])
## List of 1
##  $ 1: 'igraph.vs' Named int [1:2] 2 4
##   ..- attr(*, "names")= chr [1:2] "2" "4"
##   ..- attr(*, "env")=<weakref> 
##   ..- attr(*, "graph")= chr "c2a0ee97-c675-11e8-ad65-1fda69dbb974"

We see, the object has a list-format, consisting of sepperate lists for every node, containing some attributes which are irrelevant now, and an edgelist for every node, capturing its ego-network (eg., .. ..- attr(*, "names")= chr [1:2] "2" "4")

We can also plot it to take a look. igraph object can be directly used with the plot() function. The results can be adjusted with a set of parameters we will discover later. It’s not super pretty, therefore we will later also explore more powerfull plotting tools for rgaphs. However, its quick&dirty, so lets take it like that for now.

plot(g)

Yeah, that’s the graph. We We can also use the adjacency matrix to create the same graph.

g <- graph_from_adjacency_matrix(adj_matrix, mode = "undirected")
g
## IGRAPH c2c3c81 UN-- 4 4 -- 
## + attr: name (v/c)
## + edges from c2c3c81 (vertex names):
## [1] 1--2 1--4 2--3 2--4

We can inspect and manipulate the nodes via V(g) (V for vertices, its graph-theory slang), and edges with E(g)

V(g)
## + 4/4 vertices, named, from c2c3c81:
## [1] 1 2 3 4
E(g)
## + 4/4 edges from c2c3c81 (vertex names):
## [1] 1--2 1--4 2--3 2--4

We can also use most of the base-R slicing&dicing.

V(g)[1:3]
## + 3/4 vertices, named, from c2c3c81:
## [1] 1 2 3
E(g)[2:4]
## + 3/4 edges from c2c3c81 (vertex names):
## [1] 1--4 2--3 2--4

Remember, it’s a list-object. So, if we just want to have the values, we have to use the double bracket [[x]].

V(g)[[1:3]]
## + 3/4 vertices, named, from c2c3c81:
##   name
## 1    1
## 2    2
## 3    3

We can also use the $ notation.

V(g)$name
## [1] "1" "2" "3" "4"

Networks are coming…

Build the graph

rm(list=ls())

files <- list.files(path ="../data/GoT/", full.names = TRUE)
files
##  [1] "../data/GoT/asoiaf-all-edges.csv"   
##  [2] "../data/GoT/asoiaf-all-nodes.csv"   
##  [3] "../data/GoT/asoiaf-book1-edges.csv" 
##  [4] "../data/GoT/asoiaf-book1-nodes.csv" 
##  [5] "../data/GoT/asoiaf-book2-edges.csv" 
##  [6] "../data/GoT/asoiaf-book2-nodes.csv" 
##  [7] "../data/GoT/asoiaf-book3-edges.csv" 
##  [8] "../data/GoT/asoiaf-book3-nodes.csv" 
##  [9] "../data/GoT/asoiaf-book4-edges.csv" 
## [10] "../data/GoT/asoiaf-book4-nodes.csv" 
## [11] "../data/GoT/asoiaf-book45-edges.csv"
## [12] "../data/GoT/asoiaf-book45-nodes.csv"
## [13] "../data/GoT/asoiaf-book5-edges.csv" 
## [14] "../data/GoT/asoiaf-book5-nodes.csv" 
## [15] "../data/GoT/union_characters.RDS"   
## [16] "../data/GoT/union_edges.RDS"
edges.cooc.all <- fread(files[1], data.table = FALSE) 
head(edges.cooc.all)

So, that’s what we have, a classical edgelist, with id1 in column 1 and id2 in column2. Note, the edges are in this case weighted.I don’t like the sepperating “-” between in the names, lets get rid of them.

colnames(edges.cooc.all) <- tolower(colnames(edges.cooc.all))
edges.cooc.all %<>%
  mutate(source = gsub("-", " ", source),
         target = gsub("-", " ", target)) 

Ok, lets see how many characters we have overal.

edges.cooc.all %>%
  select(-type) %>%
  gather(x, name, source:target) %>% 
  n_distinct(.$name)
## [1] 5646
chars.main <- edges.cooc.all %>%
  select(-type) %>%
  gather(x, name, source:target) %>%
  group_by(name) %>%
  summarise(sum_weight = sum(weight)) %>%
  ungroup() %>%
  arrange(desc(sum_weight)) %>%
  top_n(50)

head(chars.main)

So far so good, if we only go by edge weights, Tyrion is going to make it…. my favorite anyhow…

However, lets reduce our edgelist to this main characters, just to warm up and keep the overview.

edges.cooc <- edges.cooc.all %>%
  filter(source %in% chars.main$name & target %in% chars.main$name) %>%
  select(source, target, weight)
g <- graph_from_data_frame(d = edges.cooc, directed = FALSE)
g
## IGRAPH c316b85 UNW- 50 402 -- 
## + attr: name (v/c), weight (e/n)
## + edges from c316b85 (vertex names):
##  [1] Aemon Targaryen (Maester Aemon)--Grenn            
##  [2] Aemon Targaryen (Maester Aemon)--Jeor Mormont     
##  [3] Aemon Targaryen (Maester Aemon)--Jon Snow         
##  [4] Aemon Targaryen (Maester Aemon)--Mance Rayder     
##  [5] Aemon Targaryen (Maester Aemon)--Robert Baratheon 
##  [6] Aemon Targaryen (Maester Aemon)--Samwell Tarly    
##  [7] Aemon Targaryen (Maester Aemon)--Stannis Baratheon
##  [8] Arya Stark                     --Bran Stark       
## + ... omitted several edges

Note that this co-occurence network is weighted (number of co-occurence), and undirected

is_weighted(g)
## [1] TRUE
is_directed(g)
## [1] FALSE

Inspect the graph

Overal graph attributes

We already know from the summary, but we can also count the number of nodes and edges as follows:

# Count number of edges
gsize(g)
## [1] 402
# Count number of vertices
gorder(g)
## [1] 50

We can give the graph a first plot to see what happens there. It’s not pretty, but we will fine-tune it later

plot(g)

We already see that some nodes are not connected (isolated), so lets drop them for our network analysis.

g <- delete_edges(g, E(g)[weight < 20])
g <- delete_vertices(g, degree(g) == 0)

Edges

# Find all edges that include "Britt"
E(g)[[inc('Daenerys Targaryen')]]  
## + 6/189 edges from c35397a (vertex names):
##                  tail               head tid hid weight
## 13    Barristan Selmy Daenerys Targaryen   3   9     83
## 68 Daenerys Targaryen              Drogo   9  11    123
## 69 Daenerys Targaryen   Hizdahr zo Loraq   9  16     96
## 70 Daenerys Targaryen      Jorah Mormont   9  24    161
## 71 Daenerys Targaryen    Quentyn Martell   9  49     58
## 72 Daenerys Targaryen   Robert Baratheon   9  39     26
# Find all pairs that spend 4 or more hours together per week
E(g)[[weight >= 150]]
## + 11/189 edges from c35397a (vertex names):
##                   tail              head tid hid weight
## 12          Arya Stark       Sansa Stark   2  43    155
## 20          Bran Stark             Hodor   4  17    209
## 26          Bran Stark        Robb Stark   4  38    169
## 55    Cersei Lannister Joffrey Baratheon   8  21    173
## 65    Cersei Lannister  Tyrion Lannister   8  47    209
## 70  Daenerys Targaryen     Jorah Mormont   9  24    161
## 83        Eddard Stark  Robert Baratheon  12  39    334
## 112       Jeor Mormont          Jon Snow  20  23    174
## 123  Joffrey Baratheon       Sansa Stark  21  43    222
## 126  Joffrey Baratheon  Tyrion Lannister  21  47    219
## 134           Jon Snow     Samwell Tarly  23  41    228
hist(E(g)$weight)

Nodes

lets see who is the most central figure in this network of interactions

Degree centrality

degree(g) 
## Aemon Targaryen (Maester Aemon)                      Arya Stark 
##                               3                               9 
##                 Barristan Selmy                      Bran Stark 
##                               5                              14 
##                Brienne of Tarth                           Bronn 
##                               4                               2 
##                   Catelyn Stark                Cersei Lannister 
##                              16                              20 
##              Daenerys Targaryen                  Davos Seaworth 
##                               6                               2 
##                           Drogo                    Eddard Stark 
##                               2                              18 
##                    Edmure Tully                  Gregor Clegane 
##                               3                               4 
##                           Grenn                Hizdahr zo Loraq 
##                               2                               2 
##                           Hodor                      Ilyn Payne 
##                               4                               4 
##                 Jaime Lannister                    Jeor Mormont 
##                              16                               4 
##               Joffrey Baratheon                      Jojen Reed 
##                              19                               3 
##                        Jon Snow                   Jorah Mormont 
##                              13                               3 
##                    Loras Tyrell                           Luwin 
##                               6                               6 
##                      Lysa Arryn                    Mance Rayder 
##                               4                               1 
##                 Margaery Tyrell                      Meera Reed 
##                               5                               3 
##                      Melisandre                     Meryn Trant 
##                               3                               3 
##              Myrcella Baratheon                   Petyr Baelish 
##                               3                              10 
##                         Pycelle                 Renly Baratheon 
##                               6                              10 
##                    Rickon Stark                      Robb Stark 
##                               6                              17 
##                Robert Baratheon                   Rodrik Cassel 
##                              16                               5 
##                   Samwell Tarly                  Sandor Clegane 
##                               5                               6 
##                     Sansa Stark               Stannis Baratheon 
##                              17                              13 
##                   Theon Greyjoy                Tommen Baratheon 
##                               6                               6 
##                Tyrion Lannister                 Tywin Lannister 
##                              25                               9 
##                 Quentyn Martell                           Varys 
##                               2                               7
which.max(degree(g))
## Tyrion Lannister 
##               47
strength(g)
## Aemon Targaryen (Maester Aemon)                      Arya Stark 
##                             234                             512 
##                 Barristan Selmy                      Bran Stark 
##                             226                            1206 
##                Brienne of Tarth                           Bronn 
##                             225                             160 
##                   Catelyn Stark                Cersei Lannister 
##                             766                            1438 
##              Daenerys Targaryen                  Davos Seaworth 
##                             547                             195 
##                           Drogo                    Eddard Stark 
##                             151                            1175 
##                    Edmure Tully                  Gregor Clegane 
##                             128                             102 
##                           Grenn                Hizdahr zo Loraq 
##                             121                             138 
##                           Hodor                      Ilyn Payne 
##                             333                             112 
##                 Jaime Lannister                    Jeor Mormont 
##                             862                             277 
##               Joffrey Baratheon                      Jojen Reed 
##                            1343                             223 
##                        Jon Snow                   Jorah Mormont 
##                            1238                             216 
##                    Loras Tyrell                           Luwin 
##                             167                             238 
##                      Lysa Arryn                    Mance Rayder 
##                             179                             112 
##                 Margaery Tyrell                      Meera Reed 
##                             258                             255 
##                      Melisandre                     Meryn Trant 
##                             208                             102 
##              Myrcella Baratheon                   Petyr Baelish 
##                              87                             477 
##                         Pycelle                 Renly Baratheon 
##                             193                             448 
##                    Rickon Stark                      Robb Stark 
##                             263                             966 
##                Robert Baratheon                   Rodrik Cassel 
##                            1091                             137 
##                   Samwell Tarly                  Sandor Clegane 
##                             472                             259 
##                     Sansa Stark               Stannis Baratheon 
##                            1059                             771 
##                   Theon Greyjoy                Tommen Baratheon 
##                             239                             337 
##                Tyrion Lannister                 Tywin Lannister 
##                            1694                             392 
##                 Quentyn Martell                           Varys 
##                              80                             400
which.max(strength(g))
## Tyrion Lannister 
##               47

Neighborhood of a Node

neighbors(g, 'Robert Baratheon')
## + 16/50 vertices, named, from c35397a:
##  [1] Barristan Selmy    Catelyn Stark      Cersei Lannister  
##  [4] Daenerys Targaryen Eddard Stark       Jaime Lannister   
##  [7] Joffrey Baratheon  Jon Snow           Petyr Baelish     
## [10] Pycelle            Renly Baratheon    Sansa Stark       
## [13] Stannis Baratheon  Tyrion Lannister   Tywin Lannister   
## [16] Varys

ego network

ego(g, 2, "Drogo")[[1]]
## + 8/50 vertices, named, from c35397a:
## [1] Drogo              Daenerys Targaryen Jorah Mormont     
## [4] Barristan Selmy    Hizdahr zo Loraq   Robert Baratheon  
## [7] Quentyn Martell    Tyrion Lannister
g.drogo <- make_ego_graph(g, 2, nodes = "Drogo")[[1]]
g.danny <- make_ego_graph(g, 2, nodes = "Daenerys Targaryen")[[1]]
plot(g.drogo)

plot(g.danny)

Btw: To merge two graphs, just do:

g.merge <- g.drogo + g.danny
g.merge
## IGRAPH c3d42f7 UN-- 21 82 -- 
## + attr: name (v/c), weight_1 (e/n), weight_2 (e/n)
## + edges from c3d42f7 (vertex names):
##  [1] Stannis Baratheon--Tywin Lannister  
##  [2] Renly Baratheon  --Stannis Baratheon
##  [3] Pycelle          --Varys            
##  [4] Petyr Baelish    --Varys            
##  [5] Petyr Baelish    --Sansa Stark      
##  [6] Petyr Baelish    --Pycelle          
##  [7] Jon Snow         --Stannis Baratheon
##  [8] Joffrey Baratheon--Varys            
## + ... omitted several edges

Betweenness Centrality

betweenness(g)
## Aemon Targaryen (Maester Aemon)                      Arya Stark 
##                             0.0                            19.0 
##                 Barristan Selmy                      Bran Stark 
##                            93.0                            17.5 
##                Brienne of Tarth                           Bronn 
##                             0.0                             0.0 
##                   Catelyn Stark                Cersei Lannister 
##                            94.0                            96.5 
##              Daenerys Targaryen                  Davos Seaworth 
##                             0.0                             0.0 
##                           Drogo                    Eddard Stark 
##                             0.0                           138.0 
##                    Edmure Tully                  Gregor Clegane 
##                             0.0                            15.0 
##                           Grenn                Hizdahr zo Loraq 
##                             0.0                             0.0 
##                           Hodor                      Ilyn Payne 
##                            85.0                             8.5 
##                 Jaime Lannister                    Jeor Mormont 
##                            67.5                            57.0 
##               Joffrey Baratheon                      Jojen Reed 
##                            63.0                             0.0 
##                        Jon Snow                   Jorah Mormont 
##                           116.0                            47.5 
##                    Loras Tyrell                           Luwin 
##                            57.5                           133.0 
##                      Lysa Arryn                    Mance Rayder 
##                             0.0                             0.0 
##                 Margaery Tyrell                      Meera Reed 
##                             0.0                             0.0 
##                      Melisandre                     Meryn Trant 
##                            19.0                             0.0 
##              Myrcella Baratheon                   Petyr Baelish 
##                             1.0                             7.0 
##                         Pycelle                 Renly Baratheon 
##                            25.0                            31.0 
##                    Rickon Stark                      Robb Stark 
##                            73.0                            98.5 
##                Robert Baratheon                   Rodrik Cassel 
##                           155.5                            25.0 
##                   Samwell Tarly                  Sandor Clegane 
##                            20.0                            13.0 
##                     Sansa Stark               Stannis Baratheon 
##                           137.0                           103.0 
##                   Theon Greyjoy                Tommen Baratheon 
##                            30.0                             0.0 
##                Tyrion Lannister                 Tywin Lannister 
##                           309.0                            24.0 
##                 Quentyn Martell                           Varys 
##                             0.0                             0.0

Eigenvector Centrality

eigen_centrality(g, scale = TRUE)$vector %>% round(3)
## Aemon Targaryen (Maester Aemon)                      Arya Stark 
##                           0.064                           0.332 
##                 Barristan Selmy                      Bran Stark 
##                           0.073                           0.297 
##                Brienne of Tarth                           Bronn 
##                           0.126                           0.183 
##                   Catelyn Stark                Cersei Lannister 
##                           0.415                           0.952 
##              Daenerys Targaryen                  Davos Seaworth 
##                           0.039                           0.060 
##                           Drogo                    Eddard Stark 
##                           0.007                           0.754 
##                    Edmure Tully                  Gregor Clegane 
##                           0.067                           0.072 
##                           Grenn                Hizdahr zo Loraq 
##                           0.035                           0.008 
##                           Hodor                      Ilyn Payne 
##                           0.079                           0.086 
##                 Jaime Lannister                    Jeor Mormont 
##                           0.547                           0.113 
##               Joffrey Baratheon                      Jojen Reed 
##                           0.922                           0.046 
##                        Jon Snow                   Jorah Mormont 
##                           0.364                           0.038 
##                    Loras Tyrell                           Luwin 
##                           0.112                           0.061 
##                      Lysa Arryn                    Mance Rayder 
##                           0.121                           0.047 
##                 Margaery Tyrell                      Meera Reed 
##                           0.200                           0.051 
##                      Melisandre                     Meryn Trant 
##                           0.065                           0.089 
##              Myrcella Baratheon                   Petyr Baelish 
##                           0.067                           0.353 
##                         Pycelle                 Renly Baratheon 
##                           0.156                           0.267 
##                    Rickon Stark                      Robb Stark 
##                           0.098                           0.428 
##                Robert Baratheon                   Rodrik Cassel 
##                           0.755                           0.044 
##                   Samwell Tarly                  Sandor Clegane 
##                           0.127                           0.186 
##                     Sansa Stark               Stannis Baratheon 
##                           0.723                           0.367 
##                   Theon Greyjoy                Tommen Baratheon 
##                           0.091                           0.270 
##                Tyrion Lannister                 Tywin Lannister 
##                           1.000                           0.333 
##                 Quentyn Martell                           Varys 
##                           0.004                           0.345

graph structure

edge_density(g)
## [1] 0.1542857
diameter(g, directed = F, weights = NA)
## [1] 4
transitivity(g)
## [1] 0.4552807
mean_distance(g, directed = F)
## [1] 2.337959

Directed Networks are comming…

So far so good, up to now we considered undirected networks, constructed by the amount characters co-occur. However, as you already might guess, that’s not where we stop.

There are also other relationships to which we can, and sometimes have to, assign a directionality. An obvious example here are family ties.

rm(chars.main, g, g.danny, g.drogo, g.merge)
edges.fam <- readRDS("../data/GoT/union_edges.RDS")
nodes.fam <- readRDS("../data/GoT/union_characters.RDS")
head(nodes.fam)
head(edges.fam)
g <- graph_from_data_frame(edges.fam, 
                           vertices = nodes.fam,
                           directed = TRUE)
g
## IGRAPH c40a0ed DN-- 208 404 -- 
## + attr: name (v/c), male (v/n), culture (v/c), house (v/c),
## | popularity (v/n), house2 (v/c), color (v/c), shape (v/c), type
## | (e/c), color (e/c), lty (e/c)
## + edges from c40a0ed (vertex names):
##  [1] Lysa Arryn       ->Robert Arryn      
##  [2] Jasper Arryn     ->Alys Arryn        
##  [3] Jasper Arryn     ->Jon Arryn         
##  [4] Jon Arryn        ->Robert Arryn      
##  [5] Cersei Lannister ->Tommen Baratheon  
##  [6] Cersei Lannister ->Joffrey Baratheon 
## + ... omitted several edges
plot(g)

For plotting the legend, I am summarizing the edge and node colors.

color_vertices <- nodes.fam %>%
  group_by(house, color) %>%
  summarise(n = n()) %>%
  filter(!is.na(color))

colors_edges <- edges.fam %>%
  group_by(type, color) %>%
  summarise(n = n()) %>%
  filter(!is.na(color))
plot(g,
     layout = layout_with_fr(g),
     vertex.label = gsub(" ", "\n", V(g)$name),
     vertex.shape = V(g)$shape,
     vertex.color = V(g)$color, 
     vertex.size = (V(g)$popularity + 0.5) * 5, 
     vertex.frame.color = "gray", 
     vertex.label.color = "black", 
     vertex.label.cex = 0.8,
     edge.arrow.size = 0.5,
     edge.color = E(g)$color,
     edge.lty = E(g)$lty)
legend("topleft", legend = c(NA, "Node color:", as.character(color_vertices$house), NA, "Edge color:", as.character(colors_edges$type)), pch = 10,
       col = c(NA, NA, color_vertices$color, NA, NA, colors_edges$color), pt.cex = 3, cex = 2, bty = "n", ncol = 1,
       title = "") 
legend("topleft", legend = "", cex = 3, bty = "n", ncol = 1,
       title = "Game of Thrones Family Ties")